Speaker 1: Pushkin. This is solvable. I'm Ronald Young Junior. Every single
Speaker 1: instance where something has transformed our society for good, there's
Speaker 1: always been this fear. Some of the greatest advancements in
Speaker 1: our society are directly linked to technological breakthroughs. Whether it
Speaker 1: was the wheel, the printing press, or the microchip, the
Speaker 1: world has been transformed from generation to generation. In many cases,
Speaker 1: transformation was not greeted with acceptance and quick adoption, but
Speaker 1: instead with anger, fear, and apprehension. And this is the
Speaker 1: reception that has met artificial intelligence, that potential negative of
Speaker 1: being reliant on this software, on AI, on robotics, that
Speaker 1: the negatives are less than the post as it is.
Speaker 1: But it's not just the fear of reliance on these technologies.
Speaker 1: It's also the fear of the technology magnifying and multiplying
Speaker 1: our worst two intendencies. There have been troubling instances where
Speaker 1: bias and robotics and AI have been identified as a
Speaker 1: potential disqualifying factor for their wide deployment. These systems have
Speaker 1: some aspect of bias, time and time and time again,
Speaker 1: they're still better than the human biases. Doctor Ayanna Howard
Speaker 1: is the Dean of the College of engineering at the
Speaker 1: Ohio State University and one of the few non white
Speaker 1: male roboticists in the field. So I don't think that
Speaker 1: these systems can ever get to zero bias, because there
Speaker 1: will always be a group that the system has not
Speaker 1: interacted with. It might be that it's perfect, as perfect
Speaker 1: is perfect, and then there's an unknown community in South
Speaker 1: Wales somewhere that had never interacted right, and now it
Speaker 1: doesn't work with them. Doctor Howard founded a robotics company
Speaker 1: called zy Robotics. They develop mobile therapies and educational products
Speaker 1: for children with special needs. She firmly believes that the
Speaker 1: proper use of AI and robotics is an invaluable asset
Speaker 1: to humanity. Underrepresentation in robotics and AI is a solvable problem.
Speaker 1: So when I was a kid, me and my sister
Speaker 1: got left at home a lot during the summers, which
Speaker 1: meant that we watched a lot of reruns on television,
Speaker 1: and one of the things that we watched all the
Speaker 1: time was The Biotic Woman, and I heard that that
Speaker 1: was a favorite of yours as well. It was um
Speaker 1: although I will tell you the Beyondic Woman when I
Speaker 1: was young was current, so I'm sure you must have
Speaker 1: saw the reruns, say but yeah, that was that was
Speaker 1: my favorite, and I think what you know, I was
Speaker 1: always into anything that was science fiction, everything from you know,
Speaker 1: Battlestar Galactica, Star Wars. Do you mean the yeah, no, yeah,
Speaker 1: the original, not the ones that they tried to remake
Speaker 1: over and over and over again. Back then it was
Speaker 1: all of it was like new right, it was like fascinating. Yeah.
Speaker 1: So The Beondic Woman was this show where this this
Speaker 1: woman was horribly mangled in like this this horrific car accident,
Speaker 1: right like accident, it was a skydiving she would have died, right,
Speaker 1: and instead the doctors took her and rebuilt her basically
Speaker 1: added the Beiondic parts and she would go around saving
Speaker 1: the world, which was was it was awesome, but she
Speaker 1: was human, like she had a personality. That was the
Speaker 1: nice thing about that. Yes, like she wasn't exactly a robot,
Speaker 1: but she was like she was a robot. It was
Speaker 1: actually if you think about it, there was only two
Speaker 1: sci fi shows that showed women in like a positive
Speaker 1: superhero light. One was wonder Woman and one was The
Speaker 1: Roonic Woman. So like one of the earliest forms of
Speaker 1: a superhero. I saw was the biotic was So I
Speaker 1: appreciate that. But tell me a little bit, how about
Speaker 1: how that watching that show kind of pique your interest
Speaker 1: in STEM. Well, so when I was watching all of
Speaker 1: these things, it also coincided with the time in middle
Speaker 1: school where you have to define the rest of your life.
Speaker 1: So they still do this, right, you have to write
Speaker 1: these essays about what do you want to do when
Speaker 1: you grow up? And so I wanted to build a
Speaker 1: broonic women. So that's what I wanted to do. Now.
Speaker 1: Of course, as my teacher said, well, building a bionic
Speaker 1: woman is not actually a career, so you have to
Speaker 1: think about, like what label do you want? And so
Speaker 1: originally I thought I wanted to be a doctor because
Speaker 1: that was who put her together. So what changed? I
Speaker 1: took biology and we were dissecting frogs and I remember
Speaker 1: we had to learn how to kill the frogs and
Speaker 1: open them up, right, I mean this was this was
Speaker 1: the days because you had to do this. Yeah, and
Speaker 1: I hated it. I mean I absolutely And I was
Speaker 1: always good at math and science, and I was like,
Speaker 1: I do not like this course. And then I thought, man,
Speaker 1: if I don't like biology, how am I going to
Speaker 1: go to med? School, How am I gonna actually start
Speaker 1: this whole process of being a doctor. But I had
Speaker 1: a teacher who said, hey, why not think about engineering?
Speaker 1: So what is it that you do now? I am
Speaker 1: a roboticist, which means my research and my practice is
Speaker 1: about building, designing, programming robots with the focus on the human,
Speaker 1: improving the human quality of life, with an even more
Speaker 1: focused initiative effort on children, so pediatrics. And you started
Speaker 1: off designing rovers for NASA. So my very first job
Speaker 1: as a roboticist, I was a robotics researcher and my
Speaker 1: task was thinking about future rovers and how do we
Speaker 1: enable them to navigate long range traversals on Mars. So
Speaker 1: how did you go from rovers to the types of
Speaker 1: robots that you're making today? You're really thinking about them
Speaker 1: in a way of being assistive towards humans. Yeah, so
Speaker 1: I think one of the things about my perception on
Speaker 1: robotics systems is really the reason why I do it
Speaker 1: is to assist us and improve our quality of life.
Speaker 1: And you know, mind you, when I used to talk
Speaker 1: about robotics, the first thing that people would say would
Speaker 1: be like, oh, you're You're the one that is taking
Speaker 1: over the jobs you're the one that took you know,
Speaker 1: a lot of people in manufacturing. I had people say, yeah,
Speaker 1: my grandfather got fired because of your robots, like that
Speaker 1: was a common thing. And I understood that from very
Speaker 1: very early on, and those were not the robots that
Speaker 1: I wanted to build in design. But I think because
Speaker 1: I always understood that I am part of an ecosystem,
Speaker 1: I am part of a community, and my responsibility is
Speaker 1: to become a contributing member of the community. And anything
Speaker 1: I did, because my talent was my mind, anything I
Speaker 1: did was about a overall positive net result versus a
Speaker 1: negative you know. I was the kid that had to
Speaker 1: go out into the neighborhood and paint over the graffiti
Speaker 1: and pick up the like that was our Saturday. Like.
Speaker 1: I didn't get to go out and play like the
Speaker 1: other kids until I had done my chores, which was
Speaker 1: you know, all of these community kind of things. And
Speaker 1: so I think I just grew up realizing that it
Speaker 1: was about community. But also my talent was my mind.
Speaker 1: It was how I designed a thought about robotics. When
Speaker 1: it comes to the types of robots you designed to
Speaker 1: help people who have disabilities, is there anything personal that
Speaker 1: happened to you that made you connect with them specifically.
Speaker 1: I had always done these stems, so science, alogy, engineering,
Speaker 1: and math camps primarily to engage girls and I'll say
Speaker 1: underrepresented minority, since that's what we were called back in
Speaker 1: the day. I had ran this one camp where there
Speaker 1: was this young lady who had a visual impairment. She
Speaker 1: was bright, bright, like smart, but the system did not
Speaker 1: work for her because it wasn't accessible. And this was
Speaker 1: the first time I'd heard this, were like, what is
Speaker 1: accessible and what is assessively technology? Because I hadn't no idea,
Speaker 1: And what it was is like I saw a problem.
Speaker 1: I was like this, like makes no sense. Why is
Speaker 1: our technology not able to be used by everyone? As
Speaker 1: an engineer, you know, I see these kinds of problems
Speaker 1: as basically a challenge to design a solution. It's just
Speaker 1: the way I'm geared and think about it. And so
Speaker 1: that started me down this rabbit hole of looking at
Speaker 1: this target demographic understanding that we as engineers aren't really
Speaker 1: addressing the needs of all the different populations. And I
Speaker 1: was always a proponent of diversity, and I didn't really
Speaker 1: understand until that moment that diversity includes raise includes ethnicity,
Speaker 1: includes gender, but it also includes ability and disability. So
Speaker 1: you have a number of apps, games, and toys all
Speaker 1: under their company, zy Robotics. Is there a particular product
Speaker 1: that you're like, you're especially proud of. I actually enjoy
Speaker 1: all of them. I will say the one that I
Speaker 1: especially like is one called Tommy the Turtle. Learn to Code.
Speaker 1: Help your kids learn the basic of coding with Tommy
Speaker 1: the Turtle. Your little one will absolutely love interacting with
Speaker 1: Tommy and his colorful friends as they gain valuable skills
Speaker 1: in programming. I can get a four year old to
Speaker 1: learn how to code and love it. And you basically
Speaker 1: have to do things like, you know, Tommy wants to
Speaker 1: play with cat, but cats too far away. Can Tommy
Speaker 1: move towards cats so they can play right? And so
Speaker 1: there's a always these this you know, goofy rhyming and stuff,
Speaker 1: and so then it'll go through like okay, in order
Speaker 1: to do this, you know, you have to put in
Speaker 1: this little button which is move one space right, and
Speaker 1: so it walks you through this and at the end
Speaker 1: it says, guess what you've done your first coding program.
Speaker 1: Now let's try it with dog. And so it basically
Speaker 1: builds up this sequential understanding sequences, understanding logic with the
Speaker 1: subjective of again having friends, playing around and things like that.
Speaker 1: And the Tommy to try to learn to code is
Speaker 1: also accessible, so if you have a children within motor disability,
Speaker 1: it actually has accessibility functions so you can use things
Speaker 1: like switch devices. If you have slight visual impairments, there
Speaker 1: are things you can do with the print. So I'm
Speaker 1: most proud of that because I've actually seen like four
Speaker 1: year olds like excited about coding, which is amazing and fascinating.
Speaker 1: What types of benefits do you think children with disabilities
Speaker 1: received from working with robots that you design? So one
Speaker 1: of the things that we know is that with anything repetition, repetition,
Speaker 1: repetition is good. The problem is is that the amount
Speaker 1: of and I'll just call it exercise, which is repetition,
Speaker 1: The amount of exercise you have to do has to
Speaker 1: be done consistently, has to be done in a repeated fashion,
Speaker 1: and typically you need someone to kind of guide you
Speaker 1: because you know, they're kids, and we just don't have
Speaker 1: enough resources. So most parents do not have are not
Speaker 1: as privilege enough to bring in a human therapist into
Speaker 1: the home every day to work with their child, And
Speaker 1: so what we focused on was designing these robotic systems
Speaker 1: that are adaptive so they have artificial intelligence in them
Speaker 1: so that they can work with the kids basically every day.
Speaker 1: So they are augmenting the services of a human therapist.
Speaker 1: But in the home environment. Did you see an uptick
Speaker 1: in people being interested in this type of technology during
Speaker 1: COVID when you wasn't so easy to have like a
Speaker 1: human aid in the house to help with children with disabilities? Yeah,
Speaker 1: so I will say yes, and that goes across the
Speaker 1: board in terms of AI, so artificial intelligence software as
Speaker 1: well as robots. There was an uptick in the use
Speaker 1: since March of twenty twenty, and it's primarily because of
Speaker 1: the fact that you know, humans were dangerous, right, like
Speaker 1: we know this. This was the whole thing is like
Speaker 1: you're shut in. You're locked in because other humans who
Speaker 1: are outside of your community, outside of your home are dangerous,
Speaker 1: whereas robots were not. And so there was this whole
Speaker 1: change and shift on the perception of robots, like robots
Speaker 1: were more of like, oh, this actually enables me to
Speaker 1: have a little bit of semblance of livelihood. And we
Speaker 1: saw an uptick in companion robots because again, you couldn't
Speaker 1: have you know, your young grandkids, for example, coming in,
Speaker 1: or therapists necessarily come in, but robots could because they
Speaker 1: were safer than people. There's folks that feel like our
Speaker 1: addiction and technology is a problem, and building technology that
Speaker 1: assists humans in whatever capacity kind of builds on that
Speaker 1: addiction a little bit, or it feeds into that addiction. Rather,
Speaker 1: what's your response to that to people that would would
Speaker 1: say that maybe we need less technology and not more. Yeah,
Speaker 1: So I actually have two answers to that. One is, yes,
Speaker 1: it does feed on these addictions. Like my robots would
Speaker 1: not work if I didn't model them based on humans
Speaker 1: propensity to interact with these systems, right, it wouldn't work.
Speaker 1: So we know that as a fact. But I would
Speaker 1: say that that potential negative of being reliant on the
Speaker 1: software on AI on robotis that the negatives are less
Speaker 1: than the positives, and the positives are. It improves so
Speaker 1: so many things. It makes access much more accessible in
Speaker 1: terms of you know, equal opportunity about jobs, education, food sources,
Speaker 1: and things like that. So the positives I think outweigh
Speaker 1: the negatives over and over and over and over again.
Speaker 1: When you see this type of interaction and you see,
Speaker 1: you know, children interacted with the technology you've created and
Speaker 1: the tools that you've created. How do you feel like,
Speaker 1: how does that make you feel to see them interacting
Speaker 1: with it successfully? My very first moment, I remember it
Speaker 1: to this day. We were this is at Georgia Tech.
Speaker 1: It was one of our very first studies. We had
Speaker 1: a gamified therapy protocol called Superpop. We had to pop
Speaker 1: these bubbles and it was linked to some movement therapy.
Speaker 1: So I remember we go into the home and there
Speaker 1: was this young child, child with cerebral palsy who was
Speaker 1: in a wheelchair and severe sebesticity, so very limited movements.
Speaker 1: And I remember the game because the game adapts to
Speaker 1: the abilities of the child, and so you know, the
Speaker 1: very first one, it started adapting and started adapting. And
Speaker 1: I remember at one instance, this child touched the bubble
Speaker 1: because it's popping bubbles, right, and I just I remember
Speaker 1: like there was this smile. It was like the sun
Speaker 1: like shone in this room. It was like all of
Speaker 1: the wats that are out there, it just shone in
Speaker 1: his face, pure joy, and it was like you you
Speaker 1: couldn't have sold it. You couldn't have bottled it. You
Speaker 1: couldn't have like bitcoined it right. It was like that
Speaker 1: pure joy that only a child could show. And at
Speaker 1: that point that was like my first moment going like,
Speaker 1: oh my gosh, I feel really really really really good.
Speaker 1: I like this feeling, I really do. It makes me
Speaker 1: realize that I'm doing the right thing, impacting directly someone's life.
Speaker 1: You're now working with children with disabilities, specifically in pediatrics,
Speaker 1: and you know you said you got there because you
Speaker 1: first wanted to work with with women, and being a
Speaker 1: woman in STEM, I know, is not the easiest path
Speaker 1: for most Do you find that you had any personal
Speaker 1: experiences that kind of led you to say that I
Speaker 1: want to kind of kick the ladder down for more
Speaker 1: women to come into this field. Yes, when I started
Speaker 1: at NASA and things like that, I was always passionate
Speaker 1: about working in with young kids. Actually, even in undergrad
Speaker 1: I tutored at the local high school and things like that.
Speaker 1: So I'd always done that just because I remember growing up,
Speaker 1: you know, engineers would come to my school and I
Speaker 1: was inspired, and so I just felt that that was
Speaker 1: a responsibility as an engineer. I didn't really feel that
Speaker 1: I had a responsibility to be quote unquote, I guess
Speaker 1: a role model until I was much older, and it
Speaker 1: was because I suddenly realized that I was tired of
Speaker 1: not seeing myself and therefore, like, I'm old, So it's
Speaker 1: not like I can produce myself right now, that's my
Speaker 1: age age, but I sure could start reaching and making
Speaker 1: sure that the generation behind me saw themselves and didn't
Speaker 1: have to go through the same things. And so it
Speaker 1: then became more of a mission than you know, things
Speaker 1: like oh I'm doing out right now here. No no, no no, no,
Speaker 1: it's a mission because it sucks when you don't see yourself.
Speaker 1: Are you speaking specifically about women? Are also specifically about
Speaker 1: black women? So I actually have three. It's women marginalized community,
Speaker 1: so black, Latin X, Indigenous, and it's also black women.
Speaker 1: So it was actually three because those three there's not
Speaker 1: a lot of us in any stages at the upper echelon,
Speaker 1: it's not a lot. How do you think enfranchising those
Speaker 1: groups that you're talking about, how do you think that's solved?
Speaker 1: Do you think it's just solved with them just seeing
Speaker 1: you in the room? Ward? What's the extra step you
Speaker 1: feel that needs to be taken in order to draw
Speaker 1: more of those people into the room. Yeah, so being
Speaker 1: able to see yourself is only the first step, but
Speaker 1: it is an important step because if you don't even
Speaker 1: see yourself, you're not even going to think that it's
Speaker 1: something for you. Right. So one is about, you know,
Speaker 1: getting this motivation. The other is about ensuring that there's opportunities.
Speaker 1: And so you know, if I see myself, but I
Speaker 1: don't have access, I don't have the curriculum in the schools,
Speaker 1: I don't know, you know, where am I supposed to
Speaker 1: go in order to learn how to do artificial intelligence
Speaker 1: or robotics? Then I might see myself, but I don't
Speaker 1: have a path for it, right, And so it's about
Speaker 1: seeing yourself, believing in that, and then having the opportunities
Speaker 1: to excel. Irrespective of your environment or how you're growing up,
Speaker 1: are those individuals who are around you. So there's a
Speaker 1: lot of people that are afraid of a future with
Speaker 1: robots at it. What would you say? How would you
Speaker 1: talk to about and and you know what, I was
Speaker 1: one of those people. I watched I robot and I
Speaker 1: was like, no, no, not like that. That. What it's
Speaker 1: easy to anything these days? What do you think the
Speaker 1: future looks like with robots in it. As a roboticist,
Speaker 1: what do you think that future? And actually, and I'm
Speaker 1: sorry to take another step back, but those those Boston
Speaker 1: Dynamics videos are really scary people when they see the
Speaker 1: robots though in the backflips. And of course we've all
Speaker 1: seen the Matrix. I'm now referencing all the movies. But
Speaker 1: what would you say to people who have a fear
Speaker 1: of the future with robots in it. The fear that
Speaker 1: people have, honestly is the fear of the uncertainty, which
Speaker 1: you're going to have irrespective of what it is. You know,
Speaker 1: I think about you know, horse and carriage and the
Speaker 1: cars came. I'm sure there were people like, oh, automobiles,
Speaker 1: you know they kill people like people die in automobiles.
Speaker 1: Why would you ever get it? Right? Like every single
Speaker 1: instance where something has transformed our society for good, there's
Speaker 1: always been this fear always always. We can go historically
Speaker 1: when this has happened. And so robots in AI, artificial
Speaker 1: intelligence is now that technology that is causing fear because
Speaker 1: of the uncertainty. And this is happening and whether you
Speaker 1: like it or not, whether you're afraid or not, it
Speaker 1: is happening, and it will accelerate period guaranteed. And so
Speaker 1: what I say is figure out how to become part
Speaker 1: of the solution. If you're you know, fearful of the
Speaker 1: I robots are feel full of, you know, bossing dynamics
Speaker 1: kind of thing, well, become a computer scientist and go
Speaker 1: work for those companies so that you don't have those
Speaker 1: kind of robots being developed. Right, Like, this is like
Speaker 1: part of the solution and also allows you to conquer
Speaker 1: your fear but also take charge of your future. I
Speaker 1: don't know why the idea of me taking years and
Speaker 1: years of trigonometry just so I could go into box
Speaker 1: of dynamics and be like, hey, what's going on in here?
Speaker 1: What do you guys do these robots just sounded very,
Speaker 1: very funny to me. But I love that. I think
Speaker 1: that's a great solution. Well someone will hopefully someone's listening.
Speaker 1: It's like, yeah, I love that idea. I'm doing it now.
Speaker 1: Can we expand on the bias and racism forbid it?
Speaker 1: Those stories do come to life, and I feel like
Speaker 1: there has been some truth to some of them in
Speaker 1: the research that I've done. Can you talk a little
Speaker 1: bit about maybe dispel some of the some of the
Speaker 1: fear that folks are having when it comes to bias
Speaker 1: and racism from artificial intelligence and front robots, Like, what
Speaker 1: exactly are we seeing there? Yeah, so there is there
Speaker 1: is truth. So a lot of the systems that are
Speaker 1: deployed that are out there have aspects of bias. And
Speaker 1: what the definition of bias is that there are differences
Speaker 1: in the results in their behaviors toward different types of
Speaker 1: people based on gender, based on racial, ethnic identity, sexual orientation, religion,
Speaker 1: like basically any type of attribute you can think about.
Speaker 1: These systems have some aspect of bias. So that's the negative.
Speaker 1: The positive, though, is that time and time and time again,
Speaker 1: they're still better than the human biases. I think our
Speaker 1: systems can be better. I honestly think we can design
Speaker 1: and build robots that can make us better humans, that
Speaker 1: can make us less biased humans. I totally believe that.
Speaker 1: But unless we really focus on that, we're just going
Speaker 1: to keep doing the same thing and propagate and just
Speaker 1: continuing the biases that we have of the past. That's
Speaker 1: what I'm afraid of. What do you say to the
Speaker 1: activists who just heard this and heard you say that
Speaker 1: robots are biased but less biased. How do you call
Speaker 1: dib down to say, to get because I'm sure what
Speaker 1: people are going to want to hear you say is
Speaker 1: like they're not biased at all. Like we get there,
Speaker 1: we get to a place where we're using these machines
Speaker 1: and the data we're feeding into them and the way
Speaker 1: that they're interacting with the world makes them get down
Speaker 1: to zero bias. Is it even possible for that to
Speaker 1: happen when they live in a world with biased humans
Speaker 1: creating robots? Yeah? No. So I don't think that these
Speaker 1: systems can ever get to zero bias because there will
Speaker 1: always be a group that the system has not interacted with.
Speaker 1: It might be that it's perfect, as perfect is perfect,
Speaker 1: and then there's an unknown community in South Wales somewhere
Speaker 1: that had never interacted, right, and now it doesn't work
Speaker 1: with them, right. So I don't think that we can
Speaker 1: ever get because as humans, we're unique and we're different,
Speaker 1: Like there's an attribute that we're going to miss. So
Speaker 1: I don't believe in zero bias. What I do believe, though,
Speaker 1: is that because these systems are based on data and
Speaker 1: they're not based on our lived experiences, it means that
Speaker 1: you can basically quiz them. You can look at them, right,
Speaker 1: So you can basically say, look, I have you know,
Speaker 1: this person who is from a rural community that is
Speaker 1: identifies as black. That's female, and I have the exact
Speaker 1: same person. But now we change from female to male.
Speaker 1: You know what's the outcome? Right, Like, you can ask
Speaker 1: that and be like, oh wait, the differences are here, Okay,
Speaker 1: we have some bias. Let's go fixes. Now you ask
Speaker 1: human the same question, it'd be kind of hard to
Speaker 1: figure out if they're biased or not, just in general.
Speaker 1: And so that's what I'm saying is it's like you
Speaker 1: can quiz the AI, and because it's based on data
Speaker 1: and an algorithm, it can give you the answer you
Speaker 1: ask a human and if they know they're biased, they're
Speaker 1: gonna lie. And if they don't know they're biased, they're
Speaker 1: just going to make up excuses because they don't realize
Speaker 1: that they are putting their own lived experience in their decision.
Speaker 1: So you would say the difference between robot bias and
Speaker 1: human bias is the ability to be I guess, radically
Speaker 1: transparent in the fact that we can open them up,
Speaker 1: look at their code and see what the answer is. Correct.
Speaker 1: Although we don't do that now. And that's why a
Speaker 1: lot of this stuff is going on about AI and
Speaker 1: biases because companies that are putting these out are not
Speaker 1: opening it up. They're not doing the assessment, they're not
Speaker 1: doing the analysis. It's other researchers like myself that are
Speaker 1: you know, third party looking at and be like, hey,
Speaker 1: there's something going on here, right it shouldn't be our
Speaker 1: role and our responsibile. But that's what we're doing for
Speaker 1: the community because the companies are not doing it for themselves.
Speaker 1: What do you think our listeners can do to kind
Speaker 1: of support this mission and improve the marketplace and kind
Speaker 1: of help more with learn more about AI and assistive technologies,
Speaker 1: but kind of also engage with it more. One thing
Speaker 1: that I think everyone has to figure out is how
Speaker 1: to code and how to program. Honestly, the jobs are changing,
Speaker 1: and even the jobs you think are, you know, well,
Speaker 1: what about law, what about policing? What about Guess what?
Speaker 1: If you don't know how to code, there's going to
Speaker 1: be a whole next generation that's going to come take
Speaker 1: your job. It's going to be humans that take it.
Speaker 1: But it's because they know how to do the new
Speaker 1: types of jobs that are going to be out there,
Speaker 1: So that's one thing everyone needs to learn how to
Speaker 1: program and the code, not be a computer scientist, but
Speaker 1: learn that as a skill set. Second is is I
Speaker 1: think people need to do a little bit more investigation
Speaker 1: of the things that are out there instead of just
Speaker 1: listening to media, because a lot of things that are
Speaker 1: out are the horror stories. So what types of publications
Speaker 1: or media sources do you think that listeners can go
Speaker 1: to in order to get accurate information about STEM, about robotics,
Speaker 1: about science and technology generally. So I actually personally like Wired.
Speaker 1: It's a technology magazine, but it is really accessible to
Speaker 1: like a general audience, and it shows both sides. So
Speaker 1: it has stories that you talk about the negatives of AI,
Speaker 1: but it also in the same article we'll talk about
Speaker 1: the positive. So I think in terms of being accessible,
Speaker 1: that's one of my favorite Doctor Howard, thank you so
Speaker 1: much for being with us. Thank you. Doctor Ayana Howard
Speaker 1: is a roboticist and dean of the College of Engineering
Speaker 1: at the Ohio State University. Be sure to check out
Speaker 1: our show notes to find out ways you can learn
Speaker 1: more about robotics and how to get involved. Next week,
Speaker 1: we're talking about vaccine passes and how they might help
Speaker 1: solve the problem of safe reentry and to some of
Speaker 1: our busiest cities. Solvable Senior Producer is Jocelyn Frank, Research
Speaker 1: by David Jah, Booking by Lisa Dunn. Our managing producer
Speaker 1: is Sasha Matthias, and our executive producer is Mio Lobel.
Speaker 1: Special thanks to Heather Fame, John Schnars, Carl Migliori, Christina Sullivan,
Speaker 1: Eric Sandler, Maggie Taylor, Emily Rostick, Maya Kanik, and Kadijah Holland.
Speaker 1: Solvable is a production of Pushkin Industries. If you like
Speaker 1: the show, please remember to share, rate, and review. It
Speaker 1: helps us find our way to the ears of new listeners.
Speaker 1: You can find Pushkin podcasts wherever you listen, including on
Speaker 1: the iHeartRadio app and Apple Podcasts. I'm Ronald Young Junior.
Speaker 1: Thanks for listening. M